{"id":"W2149237966","doi":"10.1029/2002jd003235","title":"On the determination of atmospheric water vapor from GPS measurements","year":2003,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"GNSS positioning and interference","field":"Engineering","cited_by":171,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Naval Observatory","keywords":"Global Positioning System; Zenith; Meteorology; Environmental science; Outlier; Standard deviation; Water vapor; Atmospheric model; Troposphere; Numerical weather prediction; Climatology; Geodesy; Geology; Geography; Mathematics; Computer science; Statistics; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006827413,0.0001076783,0.0001943155,0.000006354348,0.00008728744,0.00005389865,0.0002954518,0.0000504426,0.0003995835],"category_scores_gemma":[0.0005203506,0.0000593494,0.0001136566,0.0001630146,0.00009841369,0.0001368704,0.00002095406,0.0005151193,0.00009055813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009019476,"about_ca_system_score_gemma":0.00003836149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007323882,"about_ca_topic_score_gemma":0.000005924052,"domain_scores_codex":[0.9981049,0.0002620835,0.0003259526,0.00009138757,0.0009265243,0.000289173],"domain_scores_gemma":[0.9986371,0.0005382414,0.00006083205,0.0001950189,0.0004752569,0.00009349693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006759948,0.001553588,0.002797852,0.0001726773,0.0007628482,0.0000738634,0.003187332,0.01168353,0.8881873,0.01216804,0.02763256,0.05110441],"study_design_scores_gemma":[0.0009144773,0.001715918,0.01160609,0.0005530847,0.00004142956,0.00001091765,0.0004162702,0.01493657,0.9094833,0.0582326,0.001839451,0.0002499413],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897842,0.0001469813,0.00204143,0.00008793002,0.0001877762,0.00007859358,0.000001863771,0.00001063587,0.007660528],"genre_scores_gemma":[0.9981835,0.00002426367,0.001483181,0.0000156951,0.00009151611,0.000005215018,6.707698e-7,0.00001754916,0.0001783702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05085446,"threshold_uncertainty_score":0.437516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05209924624288179,"score_gpt":0.2988456210556956,"score_spread":0.2467463748128138,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}